Meta-analysis of interventions to reduce door to needle times in acute ischaemic stroke patients
Bibliographic record
Abstract
BACKGROUND: Door-to-needle time (DTN) has an important impact on thrombolysis and reperfusion outcomes in the treatment of acute ischaemic stroke. This systematic review is a critical synthesis of studies evaluating DTN reduction strategies. METHOD: Ovid MEDLINE, PubMed, Cochrane Database of Systematic Reviews, CINAHL, ProQuest dissertations and LILACS were used as bibliographic databases for primary literature. CIHI, Health Quality Council of Alberta, Health Quality Ontario and websites of heart and stroke associations in Canada, USA, UK, Australia and New Zealand were used as sources of grey literature. Searched reports were screened by title and abstract, and full texts were located for review. Articles quality was evaluated using National Institute of Health's Study Quality Assessment tools. Methods for improving DTN were categorised under 13 DTN reduction strategies, primarily adapted from the Target: Stroke Phase II recommendations, and including two additional categories: Strategies not encompassed by any Target: Stroke recommendation, and Combinations of Interventions. RESULTS: 96 studies (4 randomised control trials, 1 review, 91 observational pre/post studies) were included in the review. All strategies and interventions resulted in a reduction of DTN. Approaches using combinations of interventions were the most effective at reducing DTN (33.77% DTN reduction, standard mean difference=1.857, 95% CI=1.510-2.205), and were more effective than approaches using only a single strategy (p=0.040). DTN reduction was associated with the duration of the DTN reduction programme at each facility (p=0.006). INTERPRETATION: The greatest reductions in DTN were observed when implementing combinations of DTN reduction strategies, although there was no significant advantage to implementing more than two strategies simultaneously. PROSPERO REGISTRATION NUMBER: 42016036215.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.027 | 0.067 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.024 | 0.059 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".